Random CSV Boolean Token Cleaning Exercise Generator

Description

Sample caller-selected Boolean spellings, blank cells and unknown controls, retaining explicit classifications and nullable normalization answers from the Boolean token core.

CSV stores records as delimited text fields. Quoting can protect embedded delimiters and newlines, so a record need not equal one physical line. Cleaning field contents, interpreting their types, and preserving an exact import/export round trip are separate operations.

Sample caller-selected Boolean spellings, blank cells and unknown controls, retaining explicit classifications and nullable normalization answers from the Boolean token core.

When to use this tool

Use this generator to create reproducible csv boolean token cleaning exercise cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

Supply tokens, count in the field shapes below. Set seed when you need an explicit replay.
Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
Read cells. Use the conventions below. Keep answer-bearing fields separate when presenting an exercise.

Sample supplied tokens and delegate Boolean normalization. Return successful Boolean values, or classify rejected unknown_boolean tokens as empty when trimmed text is empty and unknown otherwise, using null for rejected values.^[1]

Delegated operations used for this result: boolean token normalizer yes no true false 1 0. Their calculation or parsing conventions apply to the corresponding result fields.^[2]

Input field	Meaning and units	Accepted input
tokens	Tokens	Required. Array of Text; maximum UTF-16 length 64.; minimum items 1; maximum items 32.
count	Count	Required. Safe integer; minimum 1; maximum 32.
seed	Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.	Optional. Safe integer.

Output field	Interpretation	Returned shape
cells	Cells	Required. Array of Object; no unknown keys.; minimum items 0; maximum items 32.
cells[].token	Token	Required. Text; maximum UTF-16 length 64.
cells[].classification	Classification	Required. Text; one of "boolean", "empty", "unknown".
cells[].normalized	Normalized	Required. Boolean. or Null.
randomness	Replay source and normalized 32-bit seed, or null seed for an advancing context source.	Required. Object; no unknown keys.
randomness.seed	Optional safe-integer replay seed; normalization and precedence are explained below.	Required. Safe integer. or Null.
randomness.source	Source	Required. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json

{
  "input": {
    "tokens": [
      "YES",
      "",
      "maybe",
      "0"
    ],
    "count": 3,
    "seed": 42
  },
  "output": {
    "cells": [
      {
        "token": "maybe",
        "classification": "unknown",
        "normalized": null
      },
      {
        "token": "",
        "classification": "empty",
        "normalized": null
      },
      {
        "token": "0",
        "classification": "boolean",
        "normalized": false
      }
    ],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

Null is not false. Case, surrounding whitespace, and supported yes/no, true/false, or 1/0 spellings follow the linked normalizer; unknown tokens are intentional records, not generator failure.
A replay requires the same complete input and implementation. The stated sampling convention applies within its eligible space. A finite draw does not exhaust all possible outputs; repeats remain possible unless a distinct-selection rule is explicit.
The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

Similar or alternative tools

Random CSV Date Column Cleaning Exercise Generator
Spell supplied Gregorian ISO dates using sampled supported four-digit-year formats, retaining the declared ISO answer and every compatible detector interpretation for ambiguous slash dates.
Random CSV Locale Number Cleaning Exercise Generator
Sample supplied decimal-comma cells under an explicit dot-grouping convention, retaining exact normalized decimal strings and the core's stable numeric sorting answer.